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4 - Implementing MLE

from Part I - Concepts, Theory, and Implementation

Published online by Cambridge University Press:  15 November 2018

Michael D. Ward  and
John S. Ahlquist
Michael D. Ward
Affiliation:
Duke University, North Carolina
John S. Ahlquist
Affiliation:
University of California, San Diego
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Summary

Provides some of the details of numerical optimization as applied to likelihood functions and discusses possible problems, both computational and data-generated.

Keywords

numerical optimizationprofile likelihoodperfect separationrare eventsNewton-RaphsonEMFisher scoringIWLS
Type
Chapter
Information
Maximum Likelihood for Social Science
Strategies for Analysis
 , pp. 79 - 94
Publisher: Cambridge University Press
Print publication year: 2018

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  • Implementing MLE
  • Michael D. Ward, Duke University, North Carolina, John S. Ahlquist, University of California, San Diego
  • Book: Maximum Likelihood for Social Science
  • Online publication: 15 November 2018
  • Chapter DOI: https://doi.org/10.1017/9781316888544.007
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  • Implementing MLE
  • Michael D. Ward, Duke University, North Carolina, John S. Ahlquist, University of California, San Diego
  • Book: Maximum Likelihood for Social Science
  • Online publication: 15 November 2018
  • Chapter DOI: https://doi.org/10.1017/9781316888544.007
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Implementing MLE
  • Michael D. Ward, Duke University, North Carolina, John S. Ahlquist, University of California, San Diego
  • Book: Maximum Likelihood for Social Science
  • Online publication: 15 November 2018
  • Chapter DOI: https://doi.org/10.1017/9781316888544.007
Available formats
×